"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.



Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import division, absolute_import
import math
from collections import OrderedDict
import torch.nn as nn
from torch.utils import model_zoo

__all__ = [
    'senet154', 'se_resnet50', 'se_resnet101', 'se_resnet152',
    'se_resnext50_32x4d', 'se_resnext101_32x4d', 'se_resnet50_fc512'
]
"""
Code imported from https://github.com/Cadene/pretrained-models.pytorch
"""

pretrained_settings = {
    'senet154': {
        'imagenet': {
            'url':
            'http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth',
            'input_space': 'RGB',
            'input_size': [3, 224, 224],
            'input_range': [0, 1],
            'mean': [0.485, 0.456, 0.406],
            'std': [0.229, 0.224, 0.225],
            'num_classes': 1000
        }
    },
    'se_resnet50': {
        'imagenet': {
            'url':
            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet50-ce0d4300.pth',
            'input_space': 'RGB',
            'input_size': [3, 224, 224],
            'input_range': [0, 1],
            'mean': [0.485, 0.456, 0.406],
            'std': [0.229, 0.224, 0.225],
            'num_classes': 1000
        }
    },
    'se_resnet101': {
        'imagenet': {
            'url':
            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet101-7e38fcc6.pth',
            'input_space': 'RGB',
            'input_size': [3, 224, 224],
            'input_range': [0, 1],
            'mean': [0.485, 0.456, 0.406],
            'std': [0.229, 0.224, 0.225],
            'num_classes': 1000
        }
    },
    'se_resnet152': {
        'imagenet': {
            'url':
            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet152-d17c99b7.pth',
            'input_space': 'RGB',
            'input_size': [3, 224, 224],
            'input_range': [0, 1],
            'mean': [0.485, 0.456, 0.406],
            'std': [0.229, 0.224, 0.225],
            'num_classes': 1000
        }
    },
    'se_resnext50_32x4d': {
        'imagenet': {
            'url':
            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth',
            'input_space': 'RGB',
            'input_size': [3, 224, 224],
            'input_range': [0, 1],
            'mean': [0.485, 0.456, 0.406],
            'std': [0.229, 0.224, 0.225],
            'num_classes': 1000
        }
    },
    'se_resnext101_32x4d': {
        'imagenet': {
            'url':
            'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth',
            'input_space': 'RGB',
            'input_size': [3, 224, 224],
            'input_range': [0, 1],
            'mean': [0.485, 0.456, 0.406],
            'std': [0.229, 0.224, 0.225],
            'num_classes': 1000
        }
    },
}


class SEModule(nn.Module):

    def __init__(self, channels, reduction):
        super(SEModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Conv2d(
            channels, channels // reduction, kernel_size=1, padding=0
        )
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(
            channels // reduction, channels, kernel_size=1, padding=0
        )
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        module_input = x
        x = self.avg_pool(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.sigmoid(x)
        return module_input * x


class Bottleneck(nn.Module):
    """
    Base class for bottlenecks that implements `forward()` method.
    """

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out = self.se_module(out) + residual
        out = self.relu(out)

        return out


class SEBottleneck(Bottleneck):
    """
    Bottleneck for SENet154.
    """
    expansion = 4

    def __init__(
        self, inplanes, planes, groups, reduction, stride=1, downsample=None
    ):
        super(SEBottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes * 2)
        self.conv2 = nn.Conv2d(
            planes * 2,
            planes * 4,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=groups,
            bias=False
        )
        self.bn2 = nn.BatchNorm2d(planes * 4)
        self.conv3 = nn.Conv2d(
            planes * 4, planes * 4, kernel_size=1, bias=False
        )
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.se_module = SEModule(planes * 4, reduction=reduction)
        self.downsample = downsample
        self.stride = stride


class SEResNetBottleneck(Bottleneck):
    """
    ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe
    implementation and uses `stride=stride` in `conv1` and not in `conv2`
    (the latter is used in the torchvision implementation of ResNet).
    """
    expansion = 4

    def __init__(
        self, inplanes, planes, groups, reduction, stride=1, downsample=None
    ):
        super(SEResNetBottleneck, self).__init__()
        self.conv1 = nn.Conv2d(
            inplanes, planes, kernel_size=1, bias=False, stride=stride
        )
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(
            planes,
            planes,
            kernel_size=3,
            padding=1,
            groups=groups,
            bias=False
        )
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.se_module = SEModule(planes * 4, reduction=reduction)
        self.downsample = downsample
        self.stride = stride


class SEResNeXtBottleneck(Bottleneck):
    """ResNeXt bottleneck type C with a Squeeze-and-Excitation module"""
    expansion = 4

    def __init__(
        self,
        inplanes,
        planes,
        groups,
        reduction,
        stride=1,
        downsample=None,
        base_width=4
    ):
        super(SEResNeXtBottleneck, self).__init__()
        width = int(math.floor(planes * (base_width/64.)) * groups)
        self.conv1 = nn.Conv2d(
            inplanes, width, kernel_size=1, bias=False, stride=1
        )
        self.bn1 = nn.BatchNorm2d(width)
        self.conv2 = nn.Conv2d(
            width,
            width,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=groups,
            bias=False
        )
        self.bn2 = nn.BatchNorm2d(width)
        self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.se_module = SEModule(planes * 4, reduction=reduction)
        self.downsample = downsample
        self.stride = stride


class SENet(nn.Module):
    """Squeeze-and-excitation network.
    
    Reference:
        Hu et al. Squeeze-and-Excitation Networks. CVPR 2018.

    Public keys:
        - ``senet154``: SENet154.
        - ``se_resnet50``: ResNet50 + SE.
        - ``se_resnet101``: ResNet101 + SE.
        - ``se_resnet152``: ResNet152 + SE.
        - ``se_resnext50_32x4d``: ResNeXt50 (groups=32, width=4) + SE.
        - ``se_resnext101_32x4d``: ResNeXt101 (groups=32, width=4) + SE.
        - ``se_resnet50_fc512``: (ResNet50 + SE) + FC.
    """

    def __init__(
        self,
        num_classes,
        loss,
        block,
        layers,
        groups,
        reduction,
        dropout_p=0.2,
        inplanes=128,
        input_3x3=True,
        downsample_kernel_size=3,
        downsample_padding=1,
        last_stride=2,
        fc_dims=None,
        **kwargs
    ):
        """
        Parameters
        ----------
        block (nn.Module): Bottleneck class.
            - For SENet154: SEBottleneck
            - For SE-ResNet models: SEResNetBottleneck
            - For SE-ResNeXt models:  SEResNeXtBottleneck
        layers (list of ints): Number of residual blocks for 4 layers of the
            network (layer1...layer4).
        groups (int): Number of groups for the 3x3 convolution in each
            bottleneck block.
            - For SENet154: 64
            - For SE-ResNet models: 1
            - For SE-ResNeXt models:  32
        reduction (int): Reduction ratio for Squeeze-and-Excitation modules.
            - For all models: 16
        dropout_p (float or None): Drop probability for the Dropout layer.
            If `None` the Dropout layer is not used.
            - For SENet154: 0.2
            - For SE-ResNet models: None
            - For SE-ResNeXt models: None
        inplanes (int):  Number of input channels for layer1.
            - For SENet154: 128
            - For SE-ResNet models: 64
            - For SE-ResNeXt models: 64
        input_3x3 (bool): If `True`, use three 3x3 convolutions instead of
            a single 7x7 convolution in layer0.
            - For SENet154: True
            - For SE-ResNet models: False
            - For SE-ResNeXt models: False
        downsample_kernel_size (int): Kernel size for downsampling convolutions
            in layer2, layer3 and layer4.
            - For SENet154: 3
            - For SE-ResNet models: 1
            - For SE-ResNeXt models: 1
        downsample_padding (int): Padding for downsampling convolutions in
            layer2, layer3 and layer4.
            - For SENet154: 1
            - For SE-ResNet models: 0
            - For SE-ResNeXt models: 0
        num_classes (int): Number of outputs in `classifier` layer.
        """
        super(SENet, self).__init__()
        self.inplanes = inplanes
        self.loss = loss

        if input_3x3:
            layer0_modules = [
                (
                    'conv1',
                    nn.Conv2d(3, 64, 3, stride=2, padding=1, bias=False)
                ),
                ('bn1', nn.BatchNorm2d(64)),
                ('relu1', nn.ReLU(inplace=True)),
                (
                    'conv2',
                    nn.Conv2d(64, 64, 3, stride=1, padding=1, bias=False)
                ),
                ('bn2', nn.BatchNorm2d(64)),
                ('relu2', nn.ReLU(inplace=True)),
                (
                    'conv3',
                    nn.Conv2d(
                        64, inplanes, 3, stride=1, padding=1, bias=False
                    )
                ),
                ('bn3', nn.BatchNorm2d(inplanes)),
                ('relu3', nn.ReLU(inplace=True)),
            ]
        else:
            layer0_modules = [
                (
                    'conv1',
                    nn.Conv2d(
                        3,
                        inplanes,
                        kernel_size=7,
                        stride=2,
                        padding=3,
                        bias=False
                    )
                ),
                ('bn1', nn.BatchNorm2d(inplanes)),
                ('relu1', nn.ReLU(inplace=True)),
            ]
        # To preserve compatibility with Caffe weights `ceil_mode=True`
        # is used instead of `padding=1`.
        layer0_modules.append(
            ('pool', nn.MaxPool2d(3, stride=2, ceil_mode=True))
        )
        self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
        self.layer1 = self._make_layer(
            block,
            planes=64,
            blocks=layers[0],
            groups=groups,
            reduction=reduction,
            downsample_kernel_size=1,
            downsample_padding=0
        )
        self.layer2 = self._make_layer(
            block,
            planes=128,
            blocks=layers[1],
            stride=2,
            groups=groups,
            reduction=reduction,
            downsample_kernel_size=downsample_kernel_size,
            downsample_padding=downsample_padding
        )
        self.layer3 = self._make_layer(
            block,
            planes=256,
            blocks=layers[2],
            stride=2,
            groups=groups,
            reduction=reduction,
            downsample_kernel_size=downsample_kernel_size,
            downsample_padding=downsample_padding
        )
        self.layer4 = self._make_layer(
            block,
            planes=512,
            blocks=layers[3],
            stride=last_stride,
            groups=groups,
            reduction=reduction,
            downsample_kernel_size=downsample_kernel_size,
            downsample_padding=downsample_padding
        )

        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
        self.fc = self._construct_fc_layer(
            fc_dims, 512 * block.expansion, dropout_p
        )
        self.classifier = nn.Linear(self.feature_dim, num_classes)

    def _make_layer(
        self,
        block,
        planes,
        blocks,
        groups,
        reduction,
        stride=1,
        downsample_kernel_size=1,
        downsample_padding=0
    ):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(
                    self.inplanes,
                    planes * block.expansion,
                    kernel_size=downsample_kernel_size,
                    stride=stride,
                    padding=downsample_padding,
                    bias=False
                ),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(
            block(
                self.inplanes, planes, groups, reduction, stride, downsample
            )
        )
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups, reduction))

        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        """
        Construct fully connected layer

        - fc_dims (list or tuple): dimensions of fc layers, if None,
                                   no fc layers are constructed
        - input_dim (int): input dimension
        - dropout_p (float): dropout probability, if None, dropout is unused
        """
        if fc_dims is None:
            self.feature_dim = input_dim
            return None

        assert isinstance(
            fc_dims, (list, tuple)
        ), 'fc_dims must be either list or tuple, but got {}'.format(
            type(fc_dims)
        )

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def featuremaps(self, x):
        x = self.layer0(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        return x

    def forward(self, x):
        f = self.featuremaps(x)
        v = self.global_avgpool(f)
        v = v.view(v.size(0), -1)

        if self.fc is not None:
            v = self.fc(v)

        if not self.training:
            return v

        y = self.classifier(v)

        if self.loss == 'softmax':
            return y
        elif self.loss == 'triplet':
            return y, v
        else:
            raise KeyError("Unsupported loss: {}".format(self.loss))


def init_pretrained_weights(model, model_url):
    """Initializes model with pretrained weights.
    
    Layers that don't match with pretrained layers in name or size are kept unchanged.
    """
    pretrain_dict = model_zoo.load_url(model_url)
    model_dict = model.state_dict()
    pretrain_dict = {
        k: v
        for k, v in pretrain_dict.items()
        if k in model_dict and model_dict[k].size() == v.size()
    }
    model_dict.update(pretrain_dict)
    model.load_state_dict(model_dict)


def senet154(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = SENet(
        num_classes=num_classes,
        loss=loss,
        block=SEBottleneck,
        layers=[3, 8, 36, 3],
        groups=64,
        reduction=16,
        dropout_p=0.2,
        last_stride=2,
        fc_dims=None,
        **kwargs
    )
    if pretrained:
        model_url = pretrained_settings['senet154']['imagenet']['url']
        init_pretrained_weights(model, model_url)
    return model


def se_resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = SENet(
        num_classes=num_classes,
        loss=loss,
        block=SEResNetBottleneck,
        layers=[3, 4, 6, 3],
        groups=1,
        reduction=16,
        dropout_p=None,
        inplanes=64,
        input_3x3=False,
        downsample_kernel_size=1,
        downsample_padding=0,
        last_stride=2,
        fc_dims=None,
        **kwargs
    )
    if pretrained:
        model_url = pretrained_settings['se_resnet50']['imagenet']['url']
        init_pretrained_weights(model, model_url)
    return model


def se_resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = SENet(
        num_classes=num_classes,
        loss=loss,
        block=SEResNetBottleneck,
        layers=[3, 4, 6, 3],
        groups=1,
        reduction=16,
        dropout_p=None,
        inplanes=64,
        input_3x3=False,
        downsample_kernel_size=1,
        downsample_padding=0,
        last_stride=1,
        fc_dims=[512],
        **kwargs
    )
    if pretrained:
        model_url = pretrained_settings['se_resnet50']['imagenet']['url']
        init_pretrained_weights(model, model_url)
    return model


def se_resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = SENet(
        num_classes=num_classes,
        loss=loss,
        block=SEResNetBottleneck,
        layers=[3, 4, 23, 3],
        groups=1,
        reduction=16,
        dropout_p=None,
        inplanes=64,
        input_3x3=False,
        downsample_kernel_size=1,
        downsample_padding=0,
        last_stride=2,
        fc_dims=None,
        **kwargs
    )
    if pretrained:
        model_url = pretrained_settings['se_resnet101']['imagenet']['url']
        init_pretrained_weights(model, model_url)
    return model


def se_resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = SENet(
        num_classes=num_classes,
        loss=loss,
        block=SEResNetBottleneck,
        layers=[3, 8, 36, 3],
        groups=1,
        reduction=16,
        dropout_p=None,
        inplanes=64,
        input_3x3=False,
        downsample_kernel_size=1,
        downsample_padding=0,
        last_stride=2,
        fc_dims=None,
        **kwargs
    )
    if pretrained:
        model_url = pretrained_settings['se_resnet152']['imagenet']['url']
        init_pretrained_weights(model, model_url)
    return model


def se_resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = SENet(
        num_classes=num_classes,
        loss=loss,
        block=SEResNeXtBottleneck,
        layers=[3, 4, 6, 3],
        groups=32,
        reduction=16,
        dropout_p=None,
        inplanes=64,
        input_3x3=False,
        downsample_kernel_size=1,
        downsample_padding=0,
        last_stride=2,
        fc_dims=None,
        **kwargs
    )
    if pretrained:
        model_url = pretrained_settings['se_resnext50_32x4d']['imagenet']['url'
                                                                          ]
        init_pretrained_weights(model, model_url)
    return model


def se_resnext101_32x4d(
    num_classes, loss='softmax', pretrained=True, **kwargs
):
    model = SENet(
        num_classes=num_classes,
        loss=loss,
        block=SEResNeXtBottleneck,
        layers=[3, 4, 23, 3],
        groups=32,
        reduction=16,
        dropout_p=None,
        inplanes=64,
        input_3x3=False,
        downsample_kernel_size=1,
        downsample_padding=0,
        last_stride=2,
        fc_dims=None,
        **kwargs
    )
    if pretrained:
        model_url = pretrained_settings['se_resnext101_32x4d']['imagenet'][
            'url']
        init_pretrained_weights(model, model_url)
    return model
